{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# MGAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import Datasets, DatasetRecord\n",
    "from timeeval.datasets import DatasetAnalyzer"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/sebastian/Documents/projects/timeeval/notebooks/data-prep/../../datasets/MGAB/data and\n",
      "saving processed datasets in /home/sebastian/Documents/projects/timeeval/notebooks/data-prep/../../datasets/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"MGAB\"\n",
    "source_folder = Path(data_raw_folder) / \"MGAB\" / \"data\"\n",
    "target_folder = data_processed_folder\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# shared by all datasets\n",
    "dataset_type = \"synthetic\"\n",
    "input_type = \"univariate\"\n",
    "datetime_index = True\n",
    "split_at = None\n",
    "train_is_normal = False\n",
    "train_type = \"unsupervised\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "dm = Datasets(target_folder)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories ../../datasets/data-processed/univariate/MGAB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed source dataset ../../datasets/MGAB/data/7.csv -> ../../datasets/data-processed/univariate/MGAB/7.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/3.csv -> ../../datasets/data-processed/univariate/MGAB/3.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/2.csv -> ../../datasets/data-processed/univariate/MGAB/2.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/6.csv -> ../../datasets/data-processed/univariate/MGAB/6.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/1.csv -> ../../datasets/data-processed/univariate/MGAB/1.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/8.csv -> ../../datasets/data-processed/univariate/MGAB/8.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/9.csv -> ../../datasets/data-processed/univariate/MGAB/9.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/5.csv -> ../../datasets/data-processed/univariate/MGAB/5.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/4.csv -> ../../datasets/data-processed/univariate/MGAB/4.test.csv\n",
      "Processed source dataset ../../datasets/MGAB/data/10.csv -> ../../datasets/data-processed/univariate/MGAB/10.test.csv\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                                               train_path  \\\ncollection_name dataset_name                                                \nKeogh           e0509m                  univariate/Keogh/e0509m.train.csv   \n                e0509m_rand_50  univariate/Keogh/e0509m_rand_50.train.csv   \n                qtdbSel100MLII  univariate/Keogh/qtdbSel100MLII.train.csv   \nMGAB            1                                                           \n                10                                                          \n                2                                                           \n                3                                                           \n                4                                                           \n                5                                                           \n                6                                                           \n                7                                                           \n                8                                                           \n                9                                                           \n\n                                                               test_path  \\\ncollection_name dataset_name                                               \nKeogh           e0509m                  univariate/Keogh/e0509m.test.csv   \n                e0509m_rand_50  univariate/Keogh/e0509m_rand_50.test.csv   \n                qtdbSel100MLII  univariate/Keogh/qtdbSel100MLII.test.csv   \nMGAB            1                             univariate/MGAB/1.test.csv   \n                10                           univariate/MGAB/10.test.csv   \n                2                             univariate/MGAB/2.test.csv   \n                3                             univariate/MGAB/3.test.csv   \n                4                             univariate/MGAB/4.test.csv   \n                5                             univariate/MGAB/5.test.csv   \n                6                             univariate/MGAB/6.test.csv   \n                7                             univariate/MGAB/7.test.csv   \n                8                             univariate/MGAB/8.test.csv   \n                9                             univariate/MGAB/9.test.csv   \n\n                               dataset_type  datetime_index split_at  \\\ncollection_name dataset_name                                           \nKeogh           e0509m                 real           False      NaN   \n                e0509m_rand_50         real           False      NaN   \n                qtdbSel100MLII         real           False      NaN   \nMGAB            1                 synthetic            True     None   \n                10                synthetic            True     None   \n                2                 synthetic            True     None   \n                3                 synthetic            True     None   \n                4                 synthetic            True     None   \n                5                 synthetic            True     None   \n                6                 synthetic            True     None   \n                7                 synthetic            True     None   \n                8                 synthetic            True     None   \n                9                 synthetic            True     None   \n\n                                     train_type  train_is_normal  input_type  \\\ncollection_name dataset_name                                                   \nKeogh           e0509m          semi-supervised             True  univariate   \n                e0509m_rand_50  semi-supervised             True  univariate   \n                qtdbSel100MLII  semi-supervised             True  univariate   \nMGAB            1                  unsupervised            False  univariate   \n                10                 unsupervised            False  univariate   \n                2                  unsupervised            False  univariate   \n                3                  unsupervised            False  univariate   \n                4                  unsupervised            False  univariate   \n                5                  unsupervised            False  univariate   \n                6                  unsupervised            False  univariate   \n                7                  unsupervised            False  univariate   \n                8                  unsupervised            False  univariate   \n                9                  unsupervised            False  univariate   \n\n                                length  dimensions  contamination  \\\ncollection_name dataset_name                                        \nKeogh           e0509m           15000           1        0.01660   \n                e0509m_rand_50   15000           1        0.01660   \n                qtdbSel100MLII   20000           1        0.01005   \nMGAB            1               100000           1        0.00200   \n                10              100000           1        0.00200   \n                2               100000           1        0.00200   \n                3               100000           1        0.00200   \n                4               100000           1        0.00200   \n                5               100000           1        0.00200   \n                6               100000           1        0.00200   \n                7               100000           1        0.00200   \n                8               100000           1        0.00200   \n                9               100000           1        0.00200   \n\n                                num_anomalies  min_anomaly_length  \\\ncollection_name dataset_name                                        \nKeogh           e0509m                      1                 249   \n                e0509m_rand_50              1                 249   \n                qtdbSel100MLII              1                 201   \nMGAB            1                          10                  20   \n                10                         10                  20   \n                2                          10                  20   \n                3                          10                  20   \n                4                          10                  20   \n                5                          10                  20   \n                6                          10                  20   \n                7                          10                  20   \n                8                          10                  20   \n                9                          10                  20   \n\n                                median_anomaly_length  max_anomaly_length  \\\ncollection_name dataset_name                                                \nKeogh           e0509m                            249                 249   \n                e0509m_rand_50                    249                 249   \n                qtdbSel100MLII                    201                 201   \nMGAB            1                                  20                  20   \n                10                                 20                  20   \n                2                                  20                  20   \n                3                                  20                  20   \n                4                                  20                  20   \n                5                                  20                  20   \n                6                                  20                  20   \n                7                                  20                  20   \n                8                                  20                  20   \n                9                                  20                  20   \n\n                                      mean     stddev     trend  \\\ncollection_name dataset_name                                      \nKeogh           e0509m         -502.867600  49.152187  no trend   \n                e0509m_rand_50 -503.138967  69.589130  no trend   \n                qtdbSel100MLII  -63.901050  36.932954  no trend   \nMGAB            1                 0.961025   0.319444  no trend   \n                10                0.960729   0.319250  no trend   \n                2                 0.960848   0.319422  no trend   \n                3                 0.960773   0.319204  no trend   \n                4                 0.961007   0.319497  no trend   \n                5                 0.960919   0.319499  no trend   \n                6                 0.960951   0.319250  no trend   \n                7                 0.960514   0.319286  no trend   \n                8                 0.960840   0.319230  no trend   \n                9                 0.960504   0.319031  no trend   \n\n                                         stationarity  \ncollection_name dataset_name                           \nKeogh           e0509m          difference_stationary  \n                e0509m_rand_50  difference_stationary  \n                qtdbSel100MLII             stationary  \nMGAB            1               difference_stationary  \n                10              difference_stationary  \n                2               difference_stationary  \n                3               difference_stationary  \n                4               difference_stationary  \n                5               difference_stationary  \n                6               difference_stationary  \n                7               difference_stationary  \n                8               difference_stationary  \n                9               difference_stationary  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>train_path</th>\n      <th>test_path</th>\n      <th>dataset_type</th>\n      <th>datetime_index</th>\n      <th>split_at</th>\n      <th>train_type</th>\n      <th>train_is_normal</th>\n      <th>input_type</th>\n      <th>length</th>\n      <th>dimensions</th>\n      <th>contamination</th>\n      <th>num_anomalies</th>\n      <th>min_anomaly_length</th>\n      <th>median_anomaly_length</th>\n      <th>max_anomaly_length</th>\n      <th>mean</th>\n      <th>stddev</th>\n      <th>trend</th>\n      <th>stationarity</th>\n    </tr>\n    <tr>\n      <th>collection_name</th>\n      <th>dataset_name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">Keogh</th>\n      <th>e0509m</th>\n      <td>univariate/Keogh/e0509m.train.csv</td>\n      <td>univariate/Keogh/e0509m.test.csv</td>\n      <td>real</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>semi-supervised</td>\n      <td>True</td>\n      <td>univariate</td>\n      <td>15000</td>\n      <td>1</td>\n      <td>0.01660</td>\n      <td>1</td>\n      <td>249</td>\n      <td>249</td>\n      <td>249</td>\n      <td>-502.867600</td>\n      <td>49.152187</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>e0509m_rand_50</th>\n      <td>univariate/Keogh/e0509m_rand_50.train.csv</td>\n      <td>univariate/Keogh/e0509m_rand_50.test.csv</td>\n      <td>real</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>semi-supervised</td>\n      <td>True</td>\n      <td>univariate</td>\n      <td>15000</td>\n      <td>1</td>\n      <td>0.01660</td>\n      <td>1</td>\n      <td>249</td>\n      <td>249</td>\n      <td>249</td>\n      <td>-503.138967</td>\n      <td>69.589130</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>qtdbSel100MLII</th>\n      <td>univariate/Keogh/qtdbSel100MLII.train.csv</td>\n      <td>univariate/Keogh/qtdbSel100MLII.test.csv</td>\n      <td>real</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>semi-supervised</td>\n      <td>True</td>\n      <td>univariate</td>\n      <td>20000</td>\n      <td>1</td>\n      <td>0.01005</td>\n      <td>1</td>\n      <td>201</td>\n      <td>201</td>\n      <td>201</td>\n      <td>-63.901050</td>\n      <td>36.932954</td>\n      <td>no trend</td>\n      <td>stationary</td>\n    </tr>\n    <tr>\n      <th rowspan=\"10\" valign=\"top\">MGAB</th>\n      <th>1</th>\n      <td></td>\n      <td>univariate/MGAB/1.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.961025</td>\n      <td>0.319444</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td></td>\n      <td>univariate/MGAB/10.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960729</td>\n      <td>0.319250</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td></td>\n      <td>univariate/MGAB/2.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960848</td>\n      <td>0.319422</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td></td>\n      <td>univariate/MGAB/3.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960773</td>\n      <td>0.319204</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td></td>\n      <td>univariate/MGAB/4.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.961007</td>\n      <td>0.319497</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td></td>\n      <td>univariate/MGAB/5.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960919</td>\n      <td>0.319499</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td></td>\n      <td>univariate/MGAB/6.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960951</td>\n      <td>0.319250</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td></td>\n      <td>univariate/MGAB/7.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960514</td>\n      <td>0.319286</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td></td>\n      <td>univariate/MGAB/8.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960840</td>\n      <td>0.319230</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td></td>\n      <td>univariate/MGAB/9.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>None</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.00200</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960504</td>\n      <td>0.319031</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly_window_margins = 10\n",
    "\n",
    "def list_regular_files(path: Path) -> List[Path]:\n",
    "    return [f for f in path.iterdir() if f.is_file()]\n",
    "\n",
    "def process_dataset(dm: Datasets, f: Path) -> None:\n",
    "    dataset_name = f.stem\n",
    "    test_filename = f\"{dataset_name}.test.csv\"\n",
    "    test_path = dataset_subfolder / test_filename\n",
    "    target_test_filepath = target_subfolder / test_filename\n",
    "    target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_PREFIX}\"\n",
    "\n",
    "    # Prepare datasets\n",
    "    df = pd.read_csv(f, index_col=0)\n",
    "    del df[\"is_ignored\"]\n",
    "    df.insert(0, \"timestamp\", pd.to_datetime(df.index, unit=\"s\"))\n",
    "\n",
    "    anomaly_points = df[df[\"is_anomaly\"] - df[\"is_anomaly\"].shift() == 1].index + 200\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    for x in anomaly_points:\n",
    "        idx = list(range(x-anomaly_window_margins, x+anomaly_window_margins))\n",
    "        df.loc[idx, \"is_anomaly\"] = 1\n",
    "    df.to_csv(target_test_filepath, index=False)\n",
    "\n",
    "    # Prepare metadata\n",
    "    da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df)\n",
    "    da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "    meta = da.metadata\n",
    "\n",
    "    dm.add_dataset(DatasetRecord(\n",
    "          collection_name=dataset_collection_name,\n",
    "          dataset_name=dataset_name,\n",
    "          train_path=\"\",\n",
    "          test_path=test_path,\n",
    "          dataset_type=dataset_type,\n",
    "          datetime_index=datetime_index,\n",
    "          split_at=split_at,\n",
    "          train_type=train_type,\n",
    "          train_is_normal=train_is_normal,\n",
    "          input_type=input_type,\n",
    "          length=meta.length,\n",
    "          dimensions=meta.dimensions,\n",
    "          contamination=meta.contamination,\n",
    "          num_anomalies=meta.num_anomalies,\n",
    "          min_anomaly_length=meta.anomaly_length.min,\n",
    "          median_anomaly_length=meta.anomaly_length.median,\n",
    "          max_anomaly_length=meta.anomaly_length.max,\n",
    "          mean=meta.mean,\n",
    "          stddev=meta.stddev,\n",
    "          trend=meta.trend,\n",
    "          stationarity=meta.get_stationarity_name(),\n",
    "    ))\n",
    "    print(f\"Processed source dataset {f} -> {target_test_filepath}\")\n",
    "\n",
    "for file in list_regular_files(source_folder):\n",
    "    process_dataset(dm, file)\n",
    "dm.save()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "                             train_path                    test_path  \\\ncollection_name dataset_name                                           \nMGAB            1                   NaN   univariate/MGAB/1.test.csv   \n                10                  NaN  univariate/MGAB/10.test.csv   \n                2                   NaN   univariate/MGAB/2.test.csv   \n                3                   NaN   univariate/MGAB/3.test.csv   \n                4                   NaN   univariate/MGAB/4.test.csv   \n                5                   NaN   univariate/MGAB/5.test.csv   \n                6                   NaN   univariate/MGAB/6.test.csv   \n                7                   NaN   univariate/MGAB/7.test.csv   \n                8                   NaN   univariate/MGAB/8.test.csv   \n                9                   NaN   univariate/MGAB/9.test.csv   \n\n                             dataset_type  datetime_index  split_at  \\\ncollection_name dataset_name                                          \nMGAB            1               synthetic            True       NaN   \n                10              synthetic            True       NaN   \n                2               synthetic            True       NaN   \n                3               synthetic            True       NaN   \n                4               synthetic            True       NaN   \n                5               synthetic            True       NaN   \n                6               synthetic            True       NaN   \n                7               synthetic            True       NaN   \n                8               synthetic            True       NaN   \n                9               synthetic            True       NaN   \n\n                                train_type  train_is_normal  input_type  \\\ncollection_name dataset_name                                              \nMGAB            1             unsupervised            False  univariate   \n                10            unsupervised            False  univariate   \n                2             unsupervised            False  univariate   \n                3             unsupervised            False  univariate   \n                4             unsupervised            False  univariate   \n                5             unsupervised            False  univariate   \n                6             unsupervised            False  univariate   \n                7             unsupervised            False  univariate   \n                8             unsupervised            False  univariate   \n                9             unsupervised            False  univariate   \n\n                              length  dimensions  contamination  \\\ncollection_name dataset_name                                      \nMGAB            1             100000           1          0.002   \n                10            100000           1          0.002   \n                2             100000           1          0.002   \n                3             100000           1          0.002   \n                4             100000           1          0.002   \n                5             100000           1          0.002   \n                6             100000           1          0.002   \n                7             100000           1          0.002   \n                8             100000           1          0.002   \n                9             100000           1          0.002   \n\n                              num_anomalies  min_anomaly_length  \\\ncollection_name dataset_name                                      \nMGAB            1                        10                  20   \n                10                       10                  20   \n                2                        10                  20   \n                3                        10                  20   \n                4                        10                  20   \n                5                        10                  20   \n                6                        10                  20   \n                7                        10                  20   \n                8                        10                  20   \n                9                        10                  20   \n\n                              median_anomaly_length  max_anomaly_length  \\\ncollection_name dataset_name                                              \nMGAB            1                                20                  20   \n                10                               20                  20   \n                2                                20                  20   \n                3                                20                  20   \n                4                                20                  20   \n                5                                20                  20   \n                6                                20                  20   \n                7                                20                  20   \n                8                                20                  20   \n                9                                20                  20   \n\n                                  mean    stddev     trend  \\\ncollection_name dataset_name                                 \nMGAB            1             0.961025  0.319444  no trend   \n                10            0.960729  0.319250  no trend   \n                2             0.960848  0.319422  no trend   \n                3             0.960773  0.319204  no trend   \n                4             0.961007  0.319497  no trend   \n                5             0.960919  0.319499  no trend   \n                6             0.960951  0.319250  no trend   \n                7             0.960514  0.319286  no trend   \n                8             0.960840  0.319230  no trend   \n                9             0.960504  0.319031  no trend   \n\n                                       stationarity  \ncollection_name dataset_name                         \nMGAB            1             difference_stationary  \n                10            difference_stationary  \n                2             difference_stationary  \n                3             difference_stationary  \n                4             difference_stationary  \n                5             difference_stationary  \n                6             difference_stationary  \n                7             difference_stationary  \n                8             difference_stationary  \n                9             difference_stationary  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>train_path</th>\n      <th>test_path</th>\n      <th>dataset_type</th>\n      <th>datetime_index</th>\n      <th>split_at</th>\n      <th>train_type</th>\n      <th>train_is_normal</th>\n      <th>input_type</th>\n      <th>length</th>\n      <th>dimensions</th>\n      <th>contamination</th>\n      <th>num_anomalies</th>\n      <th>min_anomaly_length</th>\n      <th>median_anomaly_length</th>\n      <th>max_anomaly_length</th>\n      <th>mean</th>\n      <th>stddev</th>\n      <th>trend</th>\n      <th>stationarity</th>\n    </tr>\n    <tr>\n      <th>collection_name</th>\n      <th>dataset_name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"10\" valign=\"top\">MGAB</th>\n      <th>1</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/1.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.961025</td>\n      <td>0.319444</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/10.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960729</td>\n      <td>0.319250</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/2.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960848</td>\n      <td>0.319422</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/3.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960773</td>\n      <td>0.319204</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/4.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.961007</td>\n      <td>0.319497</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/5.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960919</td>\n      <td>0.319499</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/6.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960951</td>\n      <td>0.319250</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/7.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960514</td>\n      <td>0.319286</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/8.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960840</td>\n      <td>0.319230</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NaN</td>\n      <td>univariate/MGAB/9.test.csv</td>\n      <td>synthetic</td>\n      <td>True</td>\n      <td>NaN</td>\n      <td>unsupervised</td>\n      <td>False</td>\n      <td>univariate</td>\n      <td>100000</td>\n      <td>1</td>\n      <td>0.002</td>\n      <td>10</td>\n      <td>20</td>\n      <td>20</td>\n      <td>20</td>\n      <td>0.960504</td>\n      <td>0.319031</td>\n      <td>no trend</td>\n      <td>difference_stationary</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Exploration"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "                timestamp     value  is_anomaly\n0     1970-01-01 00:00:00  0.902918           0\n1     1970-01-01 00:00:01  0.971865           0\n2     1970-01-01 00:00:02  1.047105           0\n3     1970-01-01 00:00:03  1.108403           0\n4     1970-01-01 00:00:04  1.150962           0\n...                   ...       ...         ...\n99995 1970-01-02 03:46:35  1.186836           0\n99996 1970-01-02 03:46:36  1.191163           0\n99997 1970-01-02 03:46:37  1.188584           0\n99998 1970-01-02 03:46:38  1.195656           0\n99999 1970-01-02 03:46:39  1.228565           0\n\n[100000 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>value</th>\n      <th>is_anomaly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1970-01-01 00:00:00</td>\n      <td>0.902918</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1970-01-01 00:00:01</td>\n      <td>0.971865</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1970-01-01 00:00:02</td>\n      <td>1.047105</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1970-01-01 00:00:03</td>\n      <td>1.108403</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1970-01-01 00:00:04</td>\n      <td>1.150962</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td>1970-01-02 03:46:35</td>\n      <td>1.186836</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td>1970-01-02 03:46:36</td>\n      <td>1.191163</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td>1970-01-02 03:46:37</td>\n      <td>1.188584</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>1970-01-02 03:46:38</td>\n      <td>1.195656</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>1970-01-02 03:46:39</td>\n      <td>1.228565</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(source_folder / \"1.csv\", index_col=0)\n",
    "del df[\"is_ignored\"]\n",
    "df.insert(0, \"timestamp\", pd.to_datetime(df.index, unit=\"s\"))\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"value\", \"is_anomaly\"]].plot()\n",
    "plt.xlim(32000, 33000)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "                timestamp     value  is_anomaly  is_anomaly_point\n0     1970-01-01 00:00:00  0.902918           0                 0\n1     1970-01-01 00:00:01  0.971865           0                 0\n2     1970-01-01 00:00:02  1.047105           0                 0\n3     1970-01-01 00:00:03  1.108403           0                 0\n4     1970-01-01 00:00:04  1.150962           0                 0\n...                   ...       ...         ...               ...\n99995 1970-01-02 03:46:35  1.186836           0                 0\n99996 1970-01-02 03:46:36  1.191163           0                 0\n99997 1970-01-02 03:46:37  1.188584           0                 0\n99998 1970-01-02 03:46:38  1.195656           0                 0\n99999 1970-01-02 03:46:39  1.228565           0                 0\n\n[100000 rows x 4 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>value</th>\n      <th>is_anomaly</th>\n      <th>is_anomaly_point</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1970-01-01 00:00:00</td>\n      <td>0.902918</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1970-01-01 00:00:01</td>\n      <td>0.971865</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1970-01-01 00:00:02</td>\n      <td>1.047105</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1970-01-01 00:00:03</td>\n      <td>1.108403</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1970-01-01 00:00:04</td>\n      <td>1.150962</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>99995</th>\n      <td>1970-01-02 03:46:35</td>\n      <td>1.186836</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99996</th>\n      <td>1970-01-02 03:46:36</td>\n      <td>1.191163</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99997</th>\n      <td>1970-01-02 03:46:37</td>\n      <td>1.188584</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99998</th>\n      <td>1970-01-02 03:46:38</td>\n      <td>1.195656</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>99999</th>\n      <td>1970-01-02 03:46:39</td>\n      <td>1.228565</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100000 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly_window_margins = 10\n",
    "s_change = df[\"is_anomaly\"] - df[\"is_anomaly\"].shift()\n",
    "anomaly_points = df[s_change == 1].index + 200\n",
    "df[\"is_anomaly_point\"] = 0\n",
    "for x in anomaly_points:\n",
    "    df.loc[list(range(x-anomaly_window_margins, x+anomaly_window_margins)), \"is_anomaly_point\"] = 1\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x720 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"value\", \"is_anomaly\", \"is_anomaly_point\"]].plot()\n",
    "plt.vlines([x for x in anomaly_points], ymin=0, ymax=np.max(df[\"value\"]), color=\"red\")\n",
    "plt.xlim(32200, 33200)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
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